2014 Where did they come from Genetic diversity and forensic

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RESEARCH ARTICLE
Where did they come from? Genetic diversity and forensic
investigation of the threatened palm species Butia eriospatha
Alison Gonc¸alves Nazareno • Maurı´cio Sedrez dos Reis
Received: 19 August 2013 / Accepted: 18 November 2013 / Published online: 22 November 2013
� Springer Science+Business Media Dordrecht 2013
Abstract Few studies have assessed the genetic diversity
that exists in individuals that were illegally-traded. In this
paper, we evaluate the genetic consequences of illegal
trade of the palm species Butia eriospatha. Although it is
protected by Brazilian environmental law, information
about the genetic consequences of illegal trading which can
be used to support conservation planning is still needed.
The two main questions approached were: (a) do illegally-
traded individuals have higher levels of genetic diversity
than those found in wild populations; and (b) where did the
illegally-traded individuals come from? To answer these
questions, we used nine microsatellite loci to quantify the
genetic diversity in eight wild populations (n = 390) and
one group of individuals (n = 50) planted in an urban area
of Southern Brazil. For the forensic investigation, an
assignment exclusion-test was performed. Remarkably, the
illegally-traded B. eriospatha individuals had more genetic
variation than all of the studied wild B. eriospatha popu-
lations, suggesting that there is no single target population
used by poachers. Accordingly, the multilocus assignment
test indicated that the urban B. eriospatha individuals came
from a variety of different populations, with 46 % coming
from populations not surveyed in this study. In light of
these results, we discuss the very real problem of illegal
trading of B. eriospatha that must be quickly addressed.
Our results provide information that can be used to help
support B. eriospatha conservation.
Keywords Atlantic forest � Assignment test �
Conservation genetics � Illegal trade
Introduction
Hundreds of millions of plants and animals species around
the world have been hunted and caught for food, leather,
and medicine (Kate and Laird 1999; Arroyo-Quiroz et al.
2007; Larsen and Olsen 2007) and the majority are sold to
private collectors (Alves and Filho 2007; Rosa et al. 2011;
Natusch and Lyons 2012). While some of this trade is legal
and does not harm wild populations, an alarmingly large
proportion is illegal (Redford 1992; Destro et al. 2012),
putting many wild plant and animal species on the verge of
extinction (Redford 1992; Wilkie et al. 2011).
Some examples of illegal and unsustainable wildlife
trade are well documented, such as poaching of elephants
for ivory (Wasser et al. 2004, 2010), bears for their skin,
claws and canines (Shepherd and Nijman 2008), rhinos for
their horn (Graham-Rowe 2011), and felines for their skin
and bones (Kenney et al. 1995; Check 2006). A long-term
study in India showed that at least four leopards (Panthera
pardus) have been poached every week for the past decade
(Mutterback 2012). Another problematic example comes
from Brazil where due to illegal trafficking, the bird spix’s
macaw (Cyanopsitta spixii, ararinha-azul) is now extinct
in the wild, with only 79 individuals left in the world (e.g.
Qatar, Spain, Germany and Brazil) all being raised in
captivity (Foldenauer et al. 2007). But the exploitation of
species is not a new phenomenon. During the colonial
period, the Brazilian tree ‘Pau Brasil’ (Caesalpinia
Electronic supplementary material The online version of this
article (doi:10.1007/s10592-013-0552-1) contains supplementary
material, which is available to authorized users.
A. G. Nazareno (&) � M. S. dos Reis
Nu´cleo de Pesquisas em Florestas Tropicais,
Federal University of Santa Catarina, CP 476, Floriano´polis,
Santa Catarina 88040-900, Brazil
e-mail: alison_nazareno@yahoo.com.br
123
Conserv Genet (2014) 15:441–452
DOI 10.1007/s10592-013-0552-1
echinata) was harvested and sent to Portugal in such large
quantities that the species almost became extinct (Bueno
2006). Likewise, at the beginning of the 20th century in
Southern Brazil, the population of the Brazilian pine,
Araucaria angustifolia, was almost completely decimated
(Carvalho 2006).
Around the word, the illegal trade of species and their
products is a lucrative business, providing high returns with
relatively little risk (Destro et al. 2012). In Brazil, nearly 40
million animal specimens are captured from the wild annu-
ally, representing a total retail value of approximately
US$2.5 billion a year (RENCTAS 2011). However, this
amount is an underestimate; it does not consider the illegal
trade of plants as data on plant poaching is rare. The orna-
mental plants of some botanical families (e.g. Orchidaceae,
Cactaceae, Bromeliaceae and Cyatheaceae) and timber tree
species (e.g. Swietenia macrophylla) are the most traded
plants in Brazil. According to the database of CITES (Con-
vention on International Trade in Endangered Species of
Wild Fauna and Flora that enforces regulations on Interna-
tional Trade of species) during the period from 2006 to 2010,
International trade in S. macrophylla alone reached an esti-
mated value of US$168 million (CITES 2010).
Even though some species are protected by environ-
mental laws and by International agreements, we need to
address trafficking of species from a multi-stakeholder
approach in order to inform, facilitate and support con-
servation plans and to reduce this serious threat facing
biological diversity. Furthermore, identifying and protect-
ing species that are jeopardized by illegal trade, such as the
vulnerable palm species Butia eriospatha (IUCN 2012),
can act as an insurance policy to preserve not only the
future of the species, but also the futures of the species’
ecological communities.
In Brazil, individuals of B. eriospatha have a high orna-
mental value, approximately US$3,000, an amount which is
one hundred times more than the price that poachers pay to
landowners. In Europe and North America, where this spe-
cies is also sold, its price varies depending on the stem size.
Interestingly, in a forum from one US website (http://forums.
gardenweb.com) we found the following dialogue: ‘‘We
were attracted to the B. eriospatha because they’re a real
feather palm and we want them to be at the front of our
building—along with bananas, hibiscus, etc.—to set the
tropical tone. But at this point, the nearest I’ve found any
sizable trees is Holland……… or Brazil’’. A respondent then
goes on to name an alternative source to purchase this species
in California. All B. eriospatha individuals sold abroad and
in Brazil are illegally poached, as they could not be the result
of several generations of sub-cultivation. Thereby, their
desirability and market value, as noted through the above
exchange, underscore the susceptibility of this species to
illegal trade. However, not all those who seek to purchase
them as decorative plants are aware of their vulnerability.
Furthermore, the habitat in which this vulnerable palm
occurs (highlands or campos de altitude) is not adequately
protected by conservation policies (Overbeck et al. 2007).
Even more concerning is the fact that the Atlantic Forest
(with scattered, discontinuous grassland areas, especially on
the plateaus in the southern region) has been reduced to about
7 % of its original area (Morellato and Haddad 2000).
Despite the significant fragmentation of the biome,
researchers estimate that there are at least 20,000 plant
species occurring in the biome (Myers et al. 2000), many of
which are also at severe risk of extinction.
Although B. eriospatha is protected byBrazilian law
(Instruc¸a˜o Normativa 06, MMA 2008), information about
the genetic consequences of illegal harvesting is still needed
in order to effectively support conservation programs. From
this point of view, the goals of this study were to: (i) quantify
and compare the genetic diversity of wild B. eriospatha
populations with a group of individuals that have been ille-
gally traded and are now planted in urban areas, around
luxurious homes, malls and public gardens in Southern
Brazil; (ii) estimate the genetic differentiation between
studied wild populations; and (iii) determine the originating
population of the planted urban B. eriospatha individuals. To
address these questions we used nine polymorphic micro-
satellite loci and assessed the likely originating population of
the illegally traded B. eriospatha individuals using Bayesian
assignment tests. In previous studies, nuclear microsatellites
and allozymic variation in wild populations of B. eriospatha
revealed significant genetic differentiation among popula-
tions (Reis et al. 2012; Nazareno and Reis 2013). This
regional feature of genetic variation, which is fundamental in
determining the origins of individuals by assignment tests
(Manel et al. 2002; Guinand et al. 2004), and their irregular
distribution throughout Atlantic Forest highlands allowed us
to test two linked hypotheses: (1) the illegally traded B.
eriospatha individuals come from multiple source popula-
tions since the current distribution of native plants is so
fragmented; and (2) due to their origins from multiple dif-
ferentiated populations, the illegally traded B. eriospatha
individuals have more genetic diversity than those in distinct
wild B. eriospatha populations. Our results provide impor-
tant information for decision-makers to help support con-
servation strategies of this threatened palm species as well as
combat B. eriospatha trafficking in Brazil and abroad.
Materials and methods
Study species
The slow-growing palm B. eriospatha (Fig. 1) is a
monoecious species locally known as butia´-da-serra. This
442 Conserv Genet (2014) 15:441–452
123
long-lived palm species is endemic to the Atlantic Forest
and grows in highlands (or campos de altitude, a subtype of
the Atlantic Forest Domain). Their populations, which are
restricted to this specific habitat, generally consist of
mature individuals aged 100 years or older. Populations
often occur in dense and extensive clustered distributions
(i.e. population-islands), known as butiazais (Fig. 1a).
Some populations are on roadside verges and many of them
are located on private properties. To our knowledge, there
is no B. eriospatha population protected in nature reserves.
Mating system analyses reveal that B. eriospatha
(2n = 32; Correa et al. 2009) is predominantly an out-
crossing species, although it is self-compatible and repro-
duction can occur by geitonogamy (Nazareno and Reis
2012). Illegal trafficking of B. eriospatha, along with other
threats facing the species (e.g., cattle grazing; Nazareno
and Reis 2013), have contributed significantly to the spe-
cies becoming at risk of local extinction due to a contin-
uing decrease in the number of reproductive individuals.
Sampling and study area
As the assignment test applied herein does not require
extensive sampling over the species’ native range (see
explanation below), we sampled eight of 14 wild popula-
tions of B. eriospatha located in Santa Catarina State,
Western Plateau, Southern Brazil (A–H in Fig. 2).
Although there are other B. eriospatha populations in Santa
Catarina State, we focused our sampling in populations
located within close proximity to highways (see Fig. 1a)
because we believe that these populations are more sus-
ceptible to illegal harvesting. We do not provide the exact
locations of natural populations in this study in order to
reduce the risk of poaching. A total of 360 reproductive B.
eriospatha individuals, above five meters in height, were
sampled. The number of B. eriospatha individuals sampled
per population was 29 for population D, 41 for population
C, and 50 for A, B, E, F and H. Except for populations C
and D, in which samples from all individuals were col-
lected, the B. eriospatha individuals were sampled at 50 m
intervals to avoid sampling from relatives. In addition, a
group of 50 B. eriospatha individuals (all are reproductive
and with height above 5 m) were sampled from a non-
native, urban area (X in Fig. 2). These plants were har-
vested illegally and planted in malls, and public and private
gardens (Fig. 1b) in the city of Floriano´polis, Santa Cata-
rina, Southern Brazil. The B. eriospatha population nearest
to the city of Floriano´polis is 200 km away. Considering
the species is slow-growing and it takes 60–100 years for a
B. eriospatha individual to reach five meters in height
(information obtained from interviews with landowners), it
is likely that the plants in the urban area come from natural
populations and have not been grown from seeds. All of the
natural populations included in the study have been
impacted by anthropogenic activities such as cattle farm-
ing, deforestation and the introduction of exotic species
(e.g. Pinus sp.) that are cultivated in large homogeneous
stands (Nazareno and Reis 2013).
Data analysis
The microsatellite data analyses followed two approaches.
Our primary interest was in verifying the level of genetic
diversity in wild populations as compared to a group of
illegally-traded B. eriospatha individuals. Secondly, in
order to identify the originating population of the illegally
traded B. eriospatha individuals, we checked the genetic
homogeneity of each wild population using a Bayesian
model. For forensic investigations, we conducted one
exclusion-simulation method of assignment, based on
Fig. 1 Individuals of Butia eriospatha (Martius ex Drude) Beccari in
a clustered wild population (A) and in a public garden in the city of
Floriano´polis (B), both in Santa Catarina State, Southern Brazil. The
wild population (A) is surrounded by roadways (arrow) making
access to these areas easier for illegal harvesting
Conserv Genet (2014) 15:441–452 443
123
multilocus genotype data, in order to determine the likely
origin of the illegally traded B. eriospatha individuals.
Genotyping and genetic analyses
Genomic DNA extraction from leaves was conducted using
the NucleoSpin� kit (MACHEREY–NAGEL GmbH & Co.
KG), according to the manufacturer’s instructions. Ampli-
fication protocols for nine microsatellite loci are described in
Nazareno et al. (2011). Amplification products were dena-
tured and separated with 10 % polyacrylamide gels stained
with silver nitrate. Allele sizes were estimated by compari-
son with a 10 base pair DNA ladder standard (Invitrogen,
Carlsbad, CA, USA).
Deviation from the Hardy-Weinberg equilibrium and
linkage disequilibrium were tested for each B. eriospatha
population. The significant levels for linkage equilibrium
were modified for multiple comparisons by Bonferroni
correction (Rice 1989). Allele frequencies and the fol-
lowing parameters were then calculated: allelic richness
(AR), number of private (AP) and rare alleles (R; defined as
those with a frequency of less than 5 %), observed
Fig. 2 Highlands (dark gray
areas) in the Atlantic Forest
(IBGE 2004) where Butia
eriospatha (Martius ex Drude)
Beccari can occur in Southern
Brazil (States of Parana´, Santa
Catarina and Rio Grande do
Sul). The black circles indicate
the eight natural populations
(A–H) and one urban area
(X) from which genetic samples
were obtained in Santa Catarina
State, Southern Brazil444 Conserv Genet (2014) 15:441–452
123
heterozygosity (HO), and expected heterozygosity (HE, Nei
1978). Rarefaction approach was used to standardize A to
the smallest sample size in each comparison. All of these
analyses were run using the program FSTAT 2.9.3.2
(Goudet 2002). In order to compare the average values of
AR, HO and HE between wild populations and the group of
illegally traded B. eriospatha individuals, the 95 % confi-
dence interval of the standard error of these parameters
were calculated using a jackknife procedure across all loci.
The inbreeding index (FIS) was also estimated and its
significance (determined by 10,000 permutations across all
loci) tested using the SPAGeDi program (Hardy and Ve-
kemans 2002).
The genetic differentiation was estimated using an
unbiased estimator (with respect to sample size) of FST
(Weir and Cockerham 1984) with FSTAT 2.9.3 (Goudet
2002). Null allele frequencies were assessed for all popu-
lations using the Microchecker software V 2.2.0 (van Oo-
sterhout et al. 2004). If significant homozygosity was
detected at a given locus, it was dropped and a modified
average FIS over loci was calculated. Significance was
calculated from jackknife over loci. Likewise, estimates of
genetic differentiation between populations were calcu-
lated using the ENA method (10,000 permutations)
implemented in FreeNA (Chapuis and Estoup 2007), which
corrects for the presence of null alleles. Furthermore, FST
values calculated with FSTAT 2.9.3 and FreeNA were used
to investigate isolation by distance pattern. The relation-
ship between the matrix of the logarithm of geographical
distances and the matrix of pairwise genetic distance [FST/
(1 - FST), Rousset 1997] was analysed via a Mantel’s test
(Mantel 1967) with 30,000 randomizations using the pro-
gram IBDWS 3.23 (Jensen et al. 2005).
Identification of genetic units and forensic analysis
In order to test whether B. eriospatha populations were
genetically differentiated without a priori classification of
individuals, a Bayesian model was executed in a Markov
Chain Monte Carlo (MCMC), as implemented in the struc-
ture program, version 2.3.4 (Hubisz et al. 2009). In this
model, the number of populations, K, is treated as a param-
eter processed by the MCMC scheme without any approxi-
mation providing a better estimation of K. Based on the
spatial configuration and distribution of the sampled B.
eriospatha populations and high allozyme variation between
B. eriospatha populations (FST = 0.36, Reis et al. 2012), we
performed our analysis under the assumption that the allele
frequencies in different populations are not correlated with
one another and that alleles carried at a particular locus by a
particular individual originated in some known population
(no admixture model). The K was set from 2 to 8 with each
K estimate replicated 15 times with 100,000 burn-in
iterations and 500,000 data iterations. In order to estimate the
appropriate number of populations, we estimate DK as an ad
hoc quantity related to the second order rate of change of the
log probability of data with respect to the number of clusters,
as proposed by Evanno et al. (2005).
To identify a possible source population for the illegally
traded individuals of B. eriospatha, individual assignment
tests were performed using a Bayesian multilocus-approach
(Rannala and Mountain 1997), implemented in the Gene-
Class 2.0 (Piry et al. 2004). Prior to assignment tests, we
verified the applicability of the Bayesian method using
Rannala and Mountain (1997) for our dataset in GeneClass
2.0. For this procedure, all individuals of the reference
population (the eight sampled populations) were self-clas-
sified within the sampled populations using self-assignment
(leave-one-out procedure; Efron 1983). For this approach,
the program excludes one sample from one population and
runs assignment tests against the rest of the data, calcu-
lating a mean value of the scores of each individual in the
population to which it belongs.
In the Bayesian model-based assignment test imple-
mented in GeneClass 2.0, the assumption that the true pop-
ulation of origin has been sampled is not required. The
exclusion simulation method was calculated based on the
resampling algorithm described in Paetkau et al. (2004). In
the GeneClass 2.0 program, the allele frequencies from a
sampled population are used to compute the likelihood of a
genotype occurring in the population; it compares the like-
lihood of the specific genotype to a distribution of the like-
lihoods of simulated genotypes for each investigated
population. In our analysis, the genotypes were generated by
MCMC simulations of 10,000 individuals for each of the
sampled B. eriospatha populations. In order to exclude an
individual from all but the true population of origin, one strict
criterion was chosen (p value of 0.001; i.e. if a specific
genotype is observed less than once in 1,000 randomly
simulated genotypes, the population will be excluded as the
origin). Using GeneClass2.0, we also performed an exclu-
sion test based on allele frequencies to calculate likelihoods
(Paetkau et al. 1995) to determine the most likely candidate
population from the non-excluded populations. No addi-
tional analysis to correct null alleles was performed, since
Carlsson (2008) has show that microsatellite loci affected by
null alleles do not alter the overall outcome of this test.
Results
Genetic diversity
A total of 440 individuals from the wild populations and
the urban area were surveyed (Table 1), in which 57 alleles
were identified across nine microsatellite loci. As expected,
Conserv Genet (2014) 15:441–452 445
123
the allelic richness (AR) and expected heterozygosity (HE)
in the illegally traded B. eriospatha individuals differed
significantly from the values calculated in wild populations
according to the 95 % confidence interval calculated by the
jackknife method (Table 1; Fig. S1). However, the
observed heterozygosity did not significantly differ
between the illegally traded B. eriospatha individuals and
those in wild populations (Table 1). Of the 13 private
alleles observed, 9 or 70 % were found in the urban area.
We observed a greater number of rare alleles in this group
of individuals (Table 1). Our results also indicated that B.
eriospatha plants that occur in the urban area of Flori-
ano´polis have an expected heterozygosity value slightly
higher than the B. eriospatha individuals in wild popula-
tions. The average observed heterozygosity (HO) within
wild populations was 0.36, ranging from 0.22 to 0.47
(Table 1). These values are considerably lower than the
expected heterozygosity assuming the Hardy-Weinberg
equilibrium, which averaged 0.48.
For the eight wild B. eriospatha populations, the test for
Hardy-Weinberg equilibrium found that of 144 locus-
population combinations, 46, 34 and 20, or 32.0, 23.6 and
13.9 %, showed significant deviation at p \ 0.05, 0.01 and
0.001, respectively. The test for the genotypic disequilib-
rium in all wild population samples found that 79 of 288
locus combinations or 27.4 % showed significant deviation
at the p \ 0.05; however, none of the locus pairs were
found to be in significant genotypic disequilibrium after the
Bonferroni correction (p \ 0.001).
The average FIS values were 0.25 (ranging from 0.04 to
0.46) for all studied wild B. eriospatha populations. As the FIS
was positive and significantly different from zero for all but
one population (Table 1), the excess homozygosity observed
can be due to the combined effects of null alleles (Table S1)
and inbreeding. When loci with significant null alleleswere
omitted from the analysis, the FIS values remained positive
and significantly different from zero for six of the seven
populations (Table 1). This result indicates that these six
populations likely lose allelic richness through inbreeding.
The overall estimate of genetic differentiation (Weir and
Cockerham 1984) was significant among B. eriospatha
populations (FST = 0.23, p \ 0.05). This value was similar
to the overall estimate of FST obtained after the correction
for null alleles (FST = 0.17, p \ 0.05). However, the
geographic distance among B. eriospatha populations did
not explain the pattern of genetic differentiation observed
(i.e., lack of isolation by distance, Z = 15.73, r = 0.05,
p = 0.63; Fig. 3A), indicating that there is an imbalance
between drift and migration.
Our results also indicated that null alleles inflated the
estimates of genetic distance (Fig. 3). However, even after
the correction for null alleles in the FST pairwise estimates,
no isolation by distance was observed for B. eriospatha
populations (Z = 10.79, r = 0.09, p = 0.70; Fig. 3B). The
matrix of geographic distance and the pairwise FST values
quantifying genetic differentiation among B. eriospatha
populations are presented in Table 2.
Bayesian cluster analysis
Bayesian clustering without prior information about the
geographical origins of populations showed that the highest
Table 1 Population genetics estimates for eight Butia eriospatha (Martius ex Drude) Beccari populations sampled in Santa Catarina State,
Southern Brazil. Estimates for a group of 50 B. eriospatha individuals sampled in an urban area, in Floriano´polis, Santa Catarina, are also
presented
Samples N n K AP/R AR (CI95 %) HE (CI95 %) HO (CI95 %) FIS FIS
1
A 490 50 26 0/3 2.89 (± 0.15) 0.49 (± 0.03) 0.43 (± 0.02) 0.13* 0.08*
B 610 50 30 0/5 3.33 (± 0.16) 0.47 (± 0.02) 0.42 (± 0.01) 0.12* 0.08*
C 41 41 23 0/1 2.56 (± 0.22) 0.40 (± 0.02) 0.22 (± 0.01) 0.46* 0.12*
D 29 29 28 0/4 3.11 (± 0.17) 0.49 (± 0.02) 0.47 (± 0.02) 0.04 0.00
E 120 50 29 1/2 3.22 (± 0.15) 0.52 (± 0.01) 0.35 (± 0.01) 0.32* 0.13*
F 150 50 26 0/4 2.88 (± 0.08) 0.49 (± 0.01) 0.29 (± 0.03) 0.41* 0.12*
G 40 40 25 0/2 2.67 (± 0.10) 0.47 (± 0.02) 0.29 (± 0.02) 0.38* 0.13*
H 100 50 33 2/10 3.67 (± 0.18) 0.50 (± 0.03) 0.43 (± 0.01) 0.15* -0.06
X 200 50 44 9/12 5.11 (± 0.22) 0.62 (± 0.03) 0.40 (± 0.02) nc nc
The genetic parameters AR and HE are significantly different between wild populations (A–H) and the group of illegally traded individuals
(X) according to the 95 % confidence interval
N estimate of population size, n sample size, K number of alleles, AP number of private alleles, R number of rare alleles (here defined as alleles
with a frequency of less than 5 %), AR allelic richness by rarefaction based on the minimum sample size of 29 individuals, HE and HO expected
and observed heterozygosity respectively, FIS inbreeding index, FIS
1 inbreeding index excluding the loci segregating for null alleles, CI95 % 95 %
standard error calculated by the jackknife method
* Significant at p \ 0.05. nc, not calculated
446 Conserv Genet (2014) 15:441–452
123
likelihood value (DK) occurred at K = 6 (Fig. S2), where
the number of clusters (K) was similar to the number of
wild populations sampled in this study (n = 8). Although
we expected a K equal to six due to the spatial clustering of
populations (e.g., clustering of populations A and B, and E
and F), the K value was not the result of population clus-
ters. The difference between the number of clusters and the
number of sampled populations was due to the grouping of
three populations (A, B, and H) into only one unit. While it
makes biological sense for populations A and B to be
grouped as they are located in close proximity to each other
(less than 4.0 km), this result is noteworthy because the H
population is separated from populations A and B by a
distance of 129 km. However, this result strengthens our
previous observations of lack of isolation by distance
among B. eriospatha populations. Bayesian clustering with
and without prior information about geographical origins
of populations, considering both the allele frequencies in
different populations are correlated with one another and
the admixture model (data not show), also indicated that
highest likelihood value (DK) occurred at K = 6.
Assignment tests
Considering all nine loci and the eight B. eriospatha pop-
ulations as reference data, the self-assignment tests indi-
cated that 36 % of all individuals were correctly assigned.
For eight populations, the expectation of correctly assign-
ing individuals by chance is 12.5 %. However, the mean
value of the scores from the individual self-assignment
tests was higher when we used the results of the Bayesian
cluster analysis (i.e., six populations). Fifty-three percent
0 50 100 150
0.
0
0.
2
0.
4
0 .
6
0.
8
1.
0
Geographic distance (Km)
G
en
et
ic
di
st
an
ce
A
0 50 100 150
0.
0
0.
2
0.
4
0 .
6
0 .
8
1.
0
Geographic distance (Km)
B
Fig. 3 Scatter plots of pairwise
genetic distance [FST/(1- FST)]
versus geographical distance
(Km) for eight Butia eriospatha
(Martius ex Drude) populations
sampled in Santa Catarina,
Southern Brazil. The geographic
distance among populations did
not explain the pattern of
genetic differentiation
quantified by the presence
(A) and absence of null alleles
(B)
Table 2 Matrix of the geographic distances (km; above diagonal) and the genetic differentiation (FST; below diagonal) between eight B.
eriospatha populations from Santa Catarina State, Southern Brazil, based on nine microsatellite loci
Populations A B C D E F G H
A – 3.2 26.7 100.7 127.5 123.8 21.5 128.9
B 0.039 – 25.1 97.8 126.1 122.5 20.8 129.1
C 0.355* 0.364* – 101.7 144.1 140.8 44.2 152.7
D 0.240* 0.223* 0.381* – 80.8 80.5 87.8 126.7
E 0.123* 0.196* 0.325* 0.208* – 4.4 106.2 58.3
F 0.159* 0.202* 0.367* 0.227* 0.168* – 102.5 55.3
G 0.167* 0.181* 0.435* 0.181* 0.159* 0.206* – 108.6
H 0.099* 0.035 0.358* 0.218* 0.150* 0.206* 0.157* –
B 0.027* –
C 0.205* 0.196* –
D 0.220* 0.211* 0.284* –
E 0.107* 0.184* 0.200* 0.189* –
F 0.133* 0.171* 0.191* 0.197* 0.144* –
G 0.152* 0.170* 0.254* 0.178* 0.150* 0.171* –
H 0.091* 0.040 0.206* 0.188* 0.137* 0.172* 0.137* –
In bold are the pairwise FST values using the ENA correction method as proposed by Chapuis and Estoup (2007)
Asterisks denote values that are significant at the 0.05 level
Conserv Genet (2014) 15:441–452 447
123
of all individuals were assigned correctly when populations
A, B and H were merged into one single population. For
this scenario, the expectation of correct assignment by
chance is 16.7 %. In the following analyses we consider
that the performance of individual assignment tests were
better when populations A, B and H were grouped.
The forensic analysis using the exclusion-simulation
significance test found that 24 of the B. eriospatha indi-
viduals (48 %) sampled in the urban area have an unknown
origin (p \ 0.001). For just three individuals (6 %), we
excluded all but one population as the probable population
of origin. Two individuals were assigned to the set of
populations A, B, and H. The other B. eriospatha indi-
vidual was assigned to the D population. On the other hand,
23 out of the 50 B. eriospatha individuals (46 %) may have
come from several of the six populations identified a pos-
teriori (K = 6). For instance, for one individual we
excluded only one of the six populations as not being its
probable population of origin. For these 23 individuals, the
highest score of likelihoods among the non-excluded
populations indicated populationsC, E and G as the most
likely source population for 2, 10 and 11 individuals,
respectively.
Discussion
Defined as any act that intentionally contravenes the laws
and regulations established to protect biological resources,
poaching (or illegal trade, Muth and Bowe 1998) is con-
sidered one of the most significant threats to biological
diversity (Redford 1992; Alacs et al. 2010; Wilkie et al.
2011; Destro et al. 2012). One of the main issues assessed
was whether B. eriospatha individuals that were illegally
traded and planted outside their natural area have higher
levels of genetic diversity than the ones found in wild
populations.
Interestingly, the analysis of microsatellite allelic data
revealed that the group of B. eriospatha individuals that
were illegally traded had more genetic variation (i.e. allelic
richness, expected heterozygosity) than all the studied wild
B. eriospatha populations, suggesting that there is no pre-
ferred target source population. Private and rare alleles
were also observed in greater numbers in the urban popu-
lation than the wild populations. However, as small sample
size in population genetics can impose significant analyti-
cal limitations (Nazareno and Jump 2012), we must con-
sider that the private alleles found in B. eriospatha
individuals (56 % of them being rare) from the urban area
may be so rare in the wild that they were not present in our
sample. Nevertheless, it is equally important to point out
that based on the set of microsatellite markers used here (a
total of 46 alleles in the wild populations) we believe that
our sample was adequate to detect low-frequency alleles.
To be sure, there is no specific sample size required for
such analysis; however, it is crucial for the sample to be
representative of the wider population and thus it should be
based on the degree of polymorphism of the genetic
markers used.
While we found moderate to low genetic diversity in
wild B. eriospatha populations, similar to the genetic
diversity reported for other palm species (Dowe et al. 1997;
Shapcott 1998; Perera et al. 2000; Gonza´les-Pe´rez et al.
2004; Shapcott et al. 2009; Jian et al. 2010), the continual
decrease of B. eriospatha population sizes due to illegal
trade and other deterministic factors (e.g. deforestation,
habitat degradation, cattle grazing) can jeopardize the
genetic variation that remains. For instance, population
C—one of the smallest of the populations surveyed
herein—shows the lowest levels of genetic diversity (i.e.,
expected and observed heterozygosities), which were sig-
nificantly different among all sampled wild B. eriospatha
populations (Table 1). In a recent study, Nazareno and Reis
(2013) compared genetic parameters between large and
small B. eriospatha populations for both adult plants and
seedlings. The study showed a reduction in allelic richness
in small populations. Some authors (e.g., Nei et al. 1975;
Cornuet and Luikart 1996) point out that allelic richness is
highly affected by population reduction due to the rapid
elimination of rare alleles. In the studied wild B. eriospatha
populations, the genetic consequences of human activities
may have led to a loss of alleles (e.g. there is just one allele
on locus But18 in the G population; Table S2) and may
contribute to the loss of other alleles in the near future (e.g.
while allele 149 of locus But11 could be lost in the B
population, in C population this allele could be fixed; Table
S2). Likewise, stochastic forces such as genetic drift can
contribute to the loss and fixation of alleles, mainly if the
B. eriospatha populations shrink in size and become spa-
tially isolated (Nazareno and Reis 2013). The loss of
genetic diversity due to the threats facing this palm species
is also reflected in the levels of inbreeding (i.e. fixation
index) as observed in almost all of the studied wild B.
eriospatha populations.
Consistent with the results from the analysis of genetic
diversity, the multilocus assignment exclusion-test corrob-
orates the hypothesis that illegaly traded B. eriospatha
individuals had varying origins. Even though we believe that
our sample sizes were adequate, this result should be viewed
with caution because we examined a modest number of
individuals with nine microsatellite loci (HE = 0.49). For
some species, Manel et al. (2002) reported a roughly con-
sistent result of assignment test using eight microsatellite
loci (HE = 0.60) with 30–50 individuals sampled per pop-
ulation. In a recent study, Jolivet and Degen (2012), using
just three microsatellite loci, could determine the origin of
448 Conserv Genet (2014) 15:441–452
123
sapelli timber (Entandrophragma cylindricum; meliaceae)
in the Congo Basin. Thus, the set of microsatellites used in
our analysis may have contributed to the number of indi-
viduals (n = 23) that had more than one population assigned
as the origin. Even though we were able to identify the origin
of the majority of the B. eriospatha individuals (n = 27 or
54 %), we emphasize that our analysis could be improved if
more polymorphic loci are added. This is in line with the
results of the self-assignment tests, which provided moder-
ately accurate assignments, probably due to the moderate
polymorphism of the microsatellite loci used herein.
Therefore, in order to obtain a highly accurate assignment
test of single individuals, more than nine microsatellites are
required. Furthermore, the forensic analysis for this palm
species can be better clarified if cytoplasmic markers
(chloroplast and mitochondrial) are used alongside nuclear
markers (e.g. Nazareno et al. 2011; Nazareno and Reis 2011)
to develop specific DNA profiles. DNA markers such as
those suggested above have been validated in other forensic
analyses producing reliable results in identifying the geo-
graphic origin of a specimen (Avise et al. 1987; Campbell
et al. 2003; DeYoung et al. 2003; Genton et al. 2005;
Schwenke et al. 2006; Gomez-Diaz and Gonzalez-Solis
2007; Velo-Anton et al. 2007; Sanders et al. 2008; Degen
et al. 2013; Jolivet and Degen 2012).
Although the DNA-based analysis used to determine the
origin of unknown samples has been successfully applied
for plant species (Deguilloux et al. 2004; Sarri et al. 2006;
Honjo et al. 2008; Howard et al. 2009; Nuroniah 2009;
Lowe et al. 2010; Jolivet and Degen 2012; Degen et al.
2013), there are limited discussions of their applicability
for non-timber species (Howard et al. 2009; Sarri et al.
2006) and such approaches have mainly been used to study
endangered species (Honjo et al. 2008). Nevertheless,
encouraging results from assignment tests were reported in
the analysis of the geographic origins of cultivars of the
endangered species Primula sieboldii (Honjo et al. 2008).
Whilst our study is not the first test case to apply molecular
markers to trace the geographic origin of a plant species, it
is novel in that we are attempting to identify a specific
population rather than a geographic region. In light of our
results, the inter-population differentiation across all pop-
ulations (FST = 0.17) and the FST pairwise estimates pro-
vide a moderate basis for successful assignment of B.
eriospatha individuals that were illegally harvested.
As stated by several authors (Cornuet et al. 1999; Manel
et al. 2002; Guinand et al. 2004; Degen et al. 2010), the
assignment test method is more appropriate when popula-
tions are significantly differentiated (FST [ 0.1–0.2),
although there was a case of highly successful Bayesian
assignment tests for populations with low genetic differen-
tiated on a regional scale (Jolivet and Degen 2012). Gener-
ally, theaccuracy of genotype assignment procedures
increases with increasing genetic differentiation among
populations (Cornuet et al. 1999). For example, if a poacher
claims to have obtained one B. eriospatha individual from
the C population, but we believe that the individual came
from the D population, an assignment test between the two
populations can be easily undertaken (FST between C and D
population = 0.284, Table 2). However, it can be difficult to
conduct such an analysis if this individual came from the A,
B or H population (FST = 0.03–0.09, Table 2) which were
grouped as one unit by the cluster analysis. While the
structure analysis using the Bayesian algorithm allowed us to
identify this group, the low pairwise FST values also sup-
ported this result. Furthermore, the lack of correlation
between genetic differentiation and geographic distance
suggest that an island model (Wright 1931; Maruyama
1970), rather than isolation by distance model, may best
describe the population structure of this palm species. In fact,
the island model is in line with the species’ distribution
pattern (e.g. B. eriospatha populations cover small areas with
individuals in a clustered distribution) and with their speci-
ficity by habitat (highlands). However, even though the
island model is biologically plausible for this palm species,
the number of populations likely plays a role in shaping the
population structure. As such, this issue can be better
explored when samples from other populations become
available.
Conservation perspectives
From a conservation perspective, the genetic diversity that
exists in both wild B. eriospatha populations and individuals
that occur in non-native settings should be preserved.
Although criminal charges and fines may be appropriate to
control or decrease the illegal trade of this palm species,
effective conservation strategies may be more feasible if
compensatory mitigation (e.g. seed collection for genetic
restoration) is targeted at the purchasers of illegally-traded
B. eriospatha plants. Furthermore, even though B. erio-
spatha can adapt to varying local environments, its perpet-
uation in introduced habitats, like the urban area of
Floriano´polis, can be difficult since each individual is gen-
erally isolated and surrounded by buildings, homes, or
motorways. As this palm species is able to reproduce by
selfing (Nazareno and Reis 2012), genetic diversity can be
lost in only a few generations due to inbreeding. In addition,
as previously pointed out (Clegg et al. 2002; Estoup and
Clegg 2003; Kolbe et al. 2004; Frankham 2005), coloniza-
tion following introduction into an area can lead to genetic
bottlenecks that would further reduce genetic variation. In
light of this, we emphasize that conservation strategies
should be undertaken for this palm species, such as the
creation of germplasm banks. Otherwise, even though there
is significant genetic variation in the urban B. eriospatha
Conserv Genet (2014) 15:441–452 449
123
populations, this variation will become static in a few years
because these individuals will become non-reproductive.
Others conservation strategies are also feasible for this
species, such as the sale of adult B. eriospatha individuals
that are no longer reproductive may be permitted.
In this analysis we demonstrate that the use of the
molecular tools such those employed herein may be useful in
future investigations. However, the use of numerous, rapidly
evolving DNA markers (e.g., next-generation sequencing
technology) and a database containing information about
allele frequencies for numerous, diverse samples of wild B.
eriospatha populations are necessary in order to assess those
populations severely threatened by illegal harvesting and
trafficking. Since public awareness is limited, we aspire to
develop a genetic database with additional geographic and
genetic sampling to provide wildlife enforcement officials
with a powerful conservation tool.
Acknowledgments This study is part of the Doctoral Thesis (Plant
Genetic Resources Program-Federal University of Santa Catarina,
UFSC) of the first author. We are grateful to the Nu´cleo de Pesquisas em
Florestas Tropicais (Rainforest Research Department) at UFSC for
assistance during field work. We would also like to thank the Physiology
of Development and Plant Genetics Laboratory at UFSC for providing
the infrastructure required for microsatellite analysis. The authors thank
Dr. Evelyn R. Nimmo for editing the English of the manuscript and
Renata Duzzioni for helping with Fig. 2. The authors are grateful to
FAPESC (Santa Catarina State Research Council, Brazil) and National
Counsel of Technological and Scientific Development (CNPq; to A.G.N.
and to M.S.R. 304724/210-6) for the financial support.
References
Alacs EA, Georges A, FitzSimmons NN (2010) DNA detective: a
review of molecular approaches to wildlife forensics. Forensic
Sci Med Pathol 6:180–194
Alves RRN, Filho GAP (2007) Commercialization and use of snakes
in North and Northeastern Brazil: implications for conservation
and management. Biodivers Conserv 16:969–985
Arroyo-Quiroz I, Pe´rez-Gil R, Leader-Williams (2007) Mexico in the
international reptile skin trade: a case study. Biodivers Conserv
16:931–952
Avise JC, Arnold J, Martin Ball R, Bermingham E, Lamb T, Neigel
JE et al (1987) Intraspecific phylogeography: the mitochondrial
DNA bridge between population genetics and systematics. Ann
Rev Ecol Syst 18:489–522
Bueno E (2006) Na´ufragos, traficantes e degredados: as primeiras
expedic¸o˜es ao Brasil. Editora Objetiva 313(5783):58–61
Campbell D, Duchesne P, Bernatchez L (2003) AFLP utility for
population assignment studies: analytical investigation and empir-
ical comparison with microsatellites. Mol Ecol 12:1979–1991
Carlsson J (2008) Effects of microsatellite null alleles on assignment
testing. J Heredity 99:613–616
Carvalho MMX (2006) O desmatamento das florestas de arauca´ria e o
Me´dio Vale do Iguac¸u: uma histo´ria de riqueza madeireira e
colonizac¸o˜es. Dissertation, Universidade Federal de Santa Cat-
arina, Brazil
Chapuis MP, Estoup A (2007) Microsatellite null alleles and
estimation of population differentiation. Mol Biol Evol 24:
621–631
Check E (2006) The tiger’s retreat. Nature 441:927–930
CITES—Convention on International Trade in Endangered Species
(2010) CITES Trade: a snapshot. http://www.cites.org/common/
docs/CITES-trade-snapshot-eng.pdf
Clegg SM, Degnan SM, Kikkawa J, Moritz C, Estoup A, Owens IPF
(2002) Genetic consequences of sequential founder events by an
island-colonizing bird. Proc Nat Acad Sci USA 99:8127–8132
Cornuet JM, Luikart G (1996) Description and power analysis of two
tests for inferring recent population bottlenecks from allele
frequency data. Genetics 144:2001–2014
Cornuet J-M, Piry S, Luikart G, Estoup A, Solignac M (1999) New
methods employing multilocus genotypes to select or exclude
populations as origins of individuals. Genetics 153:1989–2000
Correa LB, Barbieri RL, Rossato M, Buttow MW, Heiden G (2009)
Caracterizac¸a˜o cariolo´gica de palmeiras do genero Butia (Arec-
aceae). Rev Bras Frutic 31:1111–1116
Degen B, Holtken A, Rogge M (2010) Use of DNA-fingerprints to
control the orign of forest reproductive material. Silvae Genet
59:268–272
Degen B, Ward S, Lemes MR, Navarro C, Cavers S, Sebbenn AM
(2013) Verifying the geographic origin of mahogany (Swietenia
macrophylla King) with DNA-fingerprints. Forensic Sci Int:
Genet 7:55–62
Deguilloux MF, Pemonge MH, Petit RJ (2004) DNA based control of
oak wood geographic origin in the context of cooperage industry.
Ann For Sci 61:97–104
Destro GFG, Pimentel TL, Sabaini RM,Borges RC, Barreto R (2012)
Efforts to combat wild animals trafficking in Brazil. In: Akeem
Lameed G (ed) Biodiversity enrichment in a diverse World.
InTech, Brasilia, pp 421–436
DeYoung RW, Demarais S, Honeycutt RL, Gonzales RA, Gee KL,
Anderson JD (2003) Evaluation of a DNA microsatellite panel
useful for genetic exclusion studies in white-tailed deer. Wildlife
Soc Bull 31:220–232
Dowe JL, Benzie J, Ballment E (1997) Ecology and genetics of
Carpoxylon macrospermum H. Wendl. & Drude (Arecaceae), an
endangered palm from Vanuatu. Biol Conserv 79:205–219
Efron B (1983) Estimating the error rate of a prediction rule:
improvement on cross-validation. J Am Stat Assoc 78:316–331
Estoup A, Clegg SM (2003) Bayesian inferences on the recent island
colonization history by the bird Zosterops lateralis lateralis. Mol
Ecol 12:657–674
Evanno G, Regnaut S, Goudet J (2005) Detecting the number of
clusters of individuals using the software Structure: a simulation
study. Mol Ecol 14:2611–2620
Foldenauer U, Borjal RJ, Deb A, Arif A, Taha AS, Watson RW,
Steinmetz H, Bu¨rkle M, Hammer S (2007) Hematologic and
plasma biochemical values of Spix’s Macaws (Cyanopsitta
spixii). J Avian Med Surgery 21:275–282
Frankham R (2005) Resolving the genetic paradox in invasive
species. Heredity 94:385
Genton BJ, Shykoff A, Giraud T (2005) High genetic diversity in
French invasive populations of common ragweed, Ambrosia
artemisiifolia, as a result of multiple sources of introduction.
Mol Ecol 14:4275–4285
Gomez-Diaz E, Gonzalez-Solis J (2007) Geographic assignment of
seabirds to their origin: combining morphologic, genetic, and
biogeochemical analyses. Ecol Appl 17:1484–1498
Gonza´les-Pe´rez MA, Caujape´-Castells J, Sosa PA (2004) Allozyme
variation and structure of the Canarian endemic palm tree
Phoenix canariensis (Arecaceae): implications for conservation.
Heredity 93:307–315
450 Conserv Genet (2014) 15:441–452
123
Goudet J (2002) FSTAT: a program to estimate and test gene
diversities and fixation indices. Version 2.9.3.2. http://www2.
unil.ch/popgen/softwares/fstat.htm. Accessed 15 Dec 2012
Graham-Rowe D (2011) Endangered and in demand. Nature
480:101–103
Guinand B, Scribner KT, Topchy A, Page KS, Punch W, Burnham-
Curtis MK (2004) Sampling issues affecting accuracy of
likelihood-based classification using genetical data. Environ
Biol Fish 69:245–259
Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer
program to analyse spatial genetic structure at the individual or
population levels. Mol Ecol Notes 2:618–620
Honjo M, Ueno S, Tsumura Y, Handa T, Washitani I, Ohsawa R
(2008) Tracing the origins of stocks of the endangered species
Primula sieboldii using nuclear microsatellites and chloroplast
DNA. Conserv Genet 9:1139–1147
Howard C, Gilmore S, Robertson J, Peakall R (2009) A Cannabis
sativa STR genotype database for Australian seizures: forensic
applications and limitations. J Forensic Sci 54:556–563
Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring
weak population structure with the assistance of sample group
information. Mol Ecol Resources 9:1322–1332
IBGE—Instituto Brasileiro de Geografia e Estatı´stica (2004) Mapa
da vegetac¸a˜o do Brasil e mapa de biomas do Brasil. http://
www.ibge.gov.br
IUCN World Conservation Union (2012) Red List of Threatened
Species. Version 2010.1. http://www.iucnredlist.org. Accessed
12 Dec 2012
Jensen JL, Bohonak AJ, Kelley ST (2005) Isolation by distance, web
service. http://ibdws.sdsu.edu/. Accessed 12 Dec 2012
Jian S, Ban J, Ren H, Yan H (2010) Low genetic variation detected
within the widespread mangrove species Nypa fruticans (Pal-
mae) from Southeast Asia. Aquat Bot 92:23–27
Jolivet C, Degen B (2012) Use of DNA fingerprints to control the origin
of sapelli timber (entandrophragma cylindricum) at the forest
concession level in Cameron. Forensic Sci Int Genet 6:487–493
Kenney JS, Smith JLD, Starfield AM, McDougal CW (1995) The
long-term effects of tiger poaching on population viability.
Conserv Biol 9:1127–1133
Kolbe JJ, Glor RE, Rodriguez-Schettino L, Chamizo-Lara A, Larson
A, Losos JB (2004) Genetic variation increases during biological
invasion by a Cuban lizard. Nature 431:177–181
Kt Kate, Laird SA (1999) The commercial use of biodiversity.
Earthscan Publications Ltd, London
Larsen HO, Olsen CS (2007) Unsustainable collection and unfair
trade? Uncovering and assessing assumptions regarding central
Himalayan medicinal plant conservation. Biodivers Conserv
16:1679–1697
Lowe AJ, Wong KN, Tiong YS, Iyerh S, Chew FT (2010) A DNA
method to verify the integrity of timber supply chains; confirm-
ing the legal sourcing of merbau timber from logging concession
to sawmill. Silvae Genet 59:263–268
MMA - Ministe´rio do Meio Ambiente (2008) In: Lista oficial de
espe´cies da Flora brasileira ameac¸ada de extinc¸a˜o. Dia´rio Oficial
da Unia˜o de 24 de setembro de 2008, no 185. Sec¸a˜o 1, p. 75-83.
http://www.mma.gov.br/estruturas/ascom_boletins/_arquivos/83_
19092008034949.pdf. Accessed 8 Dec 2012
Manel S, Berthier P, Luikart G (2002) Detecting wildlife poaching:
identifying the origin of individuals with Bayesian assignment
tests and multilocus genotypes. Conserv Biol 16:650–658
Mantel N (1967) The detection of disease clustering and a generalized
regression approach. Cancer Res 27:209–220
Maruyama T (1970) Effective number of alleles in a subdivided
population. Theoret Popul Biol 1:273–306
Morellato LPC, Haddad CFB (2000) Introduction: the Brazilian
Atlantic forest. Biotropica 32:786–792
Muth R, Bowe J (1998) Illegal harvest of renewable resources in North
Amercia: towards a typology of the motivations for poaching. Soc
Nat Resour 11:9–24
Mutterback M (2012) Leopard poaching is a bigger problem in India
than previously believed. http://news.mongabay.com/2012/1030-
mutterback-leopard-poaching.html
Myers N, Mittermeier RA, Mittermeier CG, Fonseca GAB, Kent J
(2000) Biodiversity hotspots for conservation priorities. Nature
403:853–858
Natusch DJD, Lyons JA (2012) Exploited for pets: the harvest and
trade of amphibians and reptiles from Indonesian New Guinea.
Biodivers Conserv 21:2899–2911
Nazareno AG, Jump AS (2012) Species-genetic diversity correlations
in habitat fragmentation can be biased by small sample sizes.
Mol Ecol 21:2847–2849
Nazareno AG, Reis MS (2011) The same bus different: monomorphic
microsatellite markers as a new tool for genetic analysis. Am J
Bot 98:265–267
Nazareno AG, Reis MS (2012) Linking phenology to mating system:
exploring the reproductive biology of the threatened palm species
Butia eriospatha. J Heredity 103:842–852
Nazareno AG, Reis MS (2013) At risk of population decline? An
ecological and genetic approach to the threatened palm species
Butia eriospatha (Arecaceae) of Southern Brazil. J Hered doi.
doi:10.1093/jhered/est065
Nazareno AG, Zucchi MI, Reis MS (2011) Microsatellite markers for
Butia eriospatha (Arecaceae), a vulnerable palm species from
the Atlantic Rainforest of Brazil. Am J Bot 98:198–200
Nei M (1978) Estimation of average heterozygosity and genetic
distance from a small number of individuals. Genetics 89:583–590
Nei M, Maruyama T, Chakraborty R (1975) The bottleneck effect and
genetic variability in populations. Evolution 29:1–10
Nuroniah HS (2009) Diagnostic markers for the identification of the
tree species Shorea leprosula Miq. and S. parvifolia dyer and the
geographic origin of S. leprosula Miq. Dissertation, Goettingen
University
Overbeck GE, Muller SC, Fidelis A, Pfadenhauer J, Pillar VD, Blanco
CC, Boldrini II, Both R, Forneck ED (2007) Brazil’s neglected
biome: the South Brazilian Campos. Pers Plant Ecol EvolSys
9:101–116
Paetkau D, Calvert W, Stirling I, Strobeck C (1995) Microsattelite
analysis of population structure in Canadian polar bears. Mol
Ecol 4:347–354
Paetkau D, Slade R, Burden M, Estoup A (2004) Genetic assignment
methods for the direct, real-time estimation of migration rate: a
simulation-based exploration of accuracy and power. Mol Ecol
13:55–65
Perera L, Russel JR, Provan J, Powell W (2000) Use of microsatellite
DNA markers to investigate the level of genetic diversity and
population genetic structure of coconut (Cocos nucifera L.).
Genome 43:15–21
Piry S, Alapetite A, Cornuet JM, Paetkau D, Baudouin L, Estoup A
(2004) GENECLASS2: a software for genetic assignment and
first-generation migrant detection. J Hered 95:536–539
Reis MS, Mantovani A, Silva JZ, Mariot A, Bittencourt R, Nazareno
AG, Ferreira SK, Steiner F, Montagna T, Silva AALS, Fernandes
CD, Altrak G, Figueredo LG (2012) Distribuic¸a˜o da diversidade
gene´tica e conservac¸a˜o de espe´cies arbo´reas em remanescentes
florestais de Santa Catarina. In: Vibrans AC, Sevegnani L,
Gasper AL, Lingner DV (eds) Inventa´rio florı´stico florestal de
Santa Catarina. Edifurb, Blumenau
Rannala B, Mountain J (1997) Detecting immigration by using
multilocus genotypes. Proc Nat Acad Sci USA 94:9197–9201
Redford KH (1992) The empty forest. Bioscience 42:412–422
Reitz R (1974) Palmeiras—Parte 1. Flora ilustrada catarinense,
Herba´rio Barbosa Rodrigues
Conserv Genet (2014) 15:441–452 451
123
RENCTAS (2011) Rede Nacional de Combate ao Tra´fico de Animais
Silvestres. 18 Relato´rio Nacional sobre o Tra´fico de Fauna
Silvestre. http://www.renctas.org.br/. Accessed 14 Dec 2012
Rice WR (1989) Analyzing tables of statistical tests. Evolution
43:223–225
Rosa IL, Oliveira TPR, Oso´rio FM, Moraes LE, Castro ALC, Barros
GML, Alvez RRN (2011) Fisheries and trade of seahorses in
Brazil: historical perspective, current trends, and future direc-
tions. Biodivers Conserv 20:1951–1971
Rousset F (1997) Genetic differentiation and estimation of gene flow
from F-statistics under isolation by distance. Genetics 145:
1219–1228
Sanders JG, Cribbs JE, Fienberg HG, Hulburd GC, Katz LS, Palumbi
SR (2008) The tip of the tail: molecular identification of
seahorses for sale in apothecary shops and curio stores in
California. Conserv Genet 9:65–71
Sarri V, Baldoni L, Porceddu A, Cultrera NGM, Contento A, Frediani
M et al (2006) Microsatellite markers are powerful tools for
discriminating among olive cultivars and assigning them to
geographically defined populations. Genome 49:1606–1615
Schwenke PL, Rhydderch JG, Ford MJ, Marshall AR, Park LK (2006)
Forensic identification of endangered Chinook Salmon (On-
corhynchus tshawytscha) using a multilocus SNP assay. Conserv
Genet 7:983–989
Shapcott A (1998) The genetics of Ptychosperma bleeseri a rare palm
from the Northern Territory, Australia. Biol Conserv 85:203–209
Shapcott A, Dowe JL, Ford H (2009) Low genetic diversity and
recovery implications of the vulnerable Bankouale´ Palm Livis-
tona carinensis (Arecaceae), from North-eastern Africa and
Southern Arabian Peninsula. Conserv Genet 10:317–327
Shepherd CR, Nijman V (2008) The trade in bear parts from
Myanmar: an illustration of the ineffectiveness of enforcement
of international wildlife trade regulations. Biodivers Conserv
17:35–42
van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004)
Micro-checker: software for identifying and correcting genotyp-
ing errors in microsatellite data. Mol Ecol Notes 4:535–538
Velo-Anton G, Godinho R, Ayres C, Ferrand N, Rivera AC (2007)
Assignment tests applied to relocate individuals of unknown
origin in a threatened species, the European pond turtle (Emys
orbicularis). Amphibia-Reptilia 28:475–484
Wasser SK, Shedlock AM, Comstock K et al (2004) Assigning African
elephant DNA to geographic region of origin: applications to the
ivory trade. Proc Nat Acad Sci USA 101:14847–14852
Wasser S, Poole J, Lee P, Lindsay K (2010) Elephants, ivory, and
trade. Science 327:1331–1332
Weir BS, Cockerham CC (1984) Estimating F-statistics for the
analysis of population structure. Evolution 38:1358–1370
Wilkie DS, Bennett EL, Peres CA, Cunningham AA (2011) The
empty forest revisited. Ann NY Acad Sci 1223:120–128
Wright S (1931) Evolution in Mendelian populations. Genetics
16(97):159
452 Conserv Genet (2014) 15:441–452
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	Where did they come from? Genetic diversity and forensic investigation of the threatened palm species Butia eriospatha
	Abstract
	Introduction
	Materials and methods
	Study species
	Sampling and study area
	Data analysis
	Genotyping and genetic analyses
	Identification of genetic units and forensic analysis
	Results
	Genetic diversity
	Bayesian cluster analysis
	Assignment tests
	Discussion
	Conservation perspectives
	Acknowledgments
	References